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E-raamat: Bio-Inspired Computation in Telecommunications

(School of Science and Technology, Middlesex University, UK), (Lecturer, Department of Electrical and Electronic Engineering, Xian Jiaotong-L), (Associate Professor of Engineering & Technology, Multimedia University, Selangor, Malaysia)
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  • Ilmumisaeg: 11-Feb-2015
  • Kirjastus: Morgan Kaufmann Publishers In
  • Keel: eng
  • ISBN-13: 9780128017432
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 11-Feb-2015
  • Kirjastus: Morgan Kaufmann Publishers In
  • Keel: eng
  • ISBN-13: 9780128017432
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Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade.Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students.

Arvustused

"...reading this book will broaden your horizons with regard to how one could solve optimization problems by applying bio-inspired algorithms, with particular emphasis on telecommunications networks...It could be used for courses related to telecommunications, as well as for courses related to advanced algorithmics." --Computing Reviews

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Reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing
Preface xiii
List of Contributors
xv
Chapter 1 Bio-Inspired Computation and Optimization: An Overview
1(22)
1.1 Introduction
2(1)
1.2 Telecommunications and Optimization
2(2)
1.3 Key Challenges in Optimization
4(3)
1.3.1 Infinite Monkey Theorem and Heuristicity
4(1)
1.3.2 Efficiency of an Algorithm
5(1)
1.3.3 How to Choose Algorithms
5(1)
1.3.4 Time Constraints
6(1)
1.4 Bio-Inspired Optimization Algorithms
7(6)
1.4.1 SI-Based Algorithms
7(3)
1.4.2 Non-SI-Based Algorithms
10(3)
1.4.3 Other Algorithms
13(1)
1.5 Artificial Neural Networks
13(3)
1.5.1 Basic Idea
13(1)
1.5.2 Neural Networks
14(1)
1.5.3 Back Propagation Algorithm
15(1)
1.6 Support Vector Machine
16(3)
1.6.1 Linear SVM
16(2)
1.6.2 Kernel Tricks and Nonlinear SVM
18(1)
1.7 Conclusions
19(4)
References
19(4)
Chapter 2 Bio-Inspired Approaches in Telecommunications
23(20)
2.1 Introduction
23(2)
2.2 Design Problems in Telecommunications
25(2)
2.3 Green communications
27(6)
2.3.1 Energy Consumption in Wireless Communications
28(1)
2.3.2 Metrics for Energy Efficiency
29(2)
2.3.3 Radio Resource Management
31(1)
2.3.4 Strategic Network Deployment
32(1)
2.4 Orthogonal Frequency Division Multiplexing
33(2)
2.4.1 OFDM Systems
33(1)
2.4.2 Three-Step Procedure for Timing and Frequency Synchronization
34(1)
2.5 OFDMA Model Considering Energy Efficiency and Quality-of-Service
35(3)
2.5.1 Mathematical Formulation
35(2)
2.5.2 Results
37(1)
2.6 Conclusions
38(5)
References
38(5)
Chapter 3 Firefly Algorithm in Telecommunications
43(30)
3.1 Introduction
44(2)
3.2 Firefly Algorithm
46(3)
3.2.1 Algorithm Complexity
48(1)
3.2.2 Variants of Firefly Algorithm
48(1)
3.3 Traffic Characterization
49(6)
3.3.1 Network Management Based on Flow Analysis and Traffic Characterization
51(1)
3.3.2 Firefly Harmonic Clustering Algorithm
52(2)
3.3.3 Results
54(1)
3.4 Applications in Wireless Cooperative Networks
55(15)
3.4.1 Related Work
58(1)
3.4.2 System Model and Problem Statement
59(3)
3.4.3 Dinkelbach Method
62(2)
3.4.4 Firefly Algorithm
64(1)
3.4.5 Simulations and Numerical Results
65(5)
3.5 Concluding Remarks
70(3)
3.5.1 FA in Traffic Characterization
70(1)
3.5.2 FA in Cooperative Networks
70(1)
References
70(3)
Chapter 4 A Survey of Intrusion Detection Systems Using Evolutionary Computation
73(22)
4.1 Introduction
73(2)
4.2 Intrusion Detection Systems
75(4)
4.2.1 IDS Components
76(2)
4.2.2 Research Areas and Challenges in Intrusion Detection
78(1)
4.3 The Method: Evolutionary Computation
79(1)
4.4 Evolutionary Computation Applications on Intrusion Detection
80(9)
4.4.1 Foundations
80(1)
4.4.2 Data Collection
81(2)
4.4.3 Detection Techniques and Response
83(3)
4.4.4 IDS Architecture
86(2)
4.4.5 IDS Security
88(1)
4.4.6 Testing and Evaluation
88(1)
4.5 Conclusion and Future Directions
89(6)
Acknowledgments
90(1)
References
91(4)
Chapter 5 VoIP Quality Prediction Model by Bio-Inspired Methods
95(22)
5.1 Introduction
96(1)
5.2 Speech Quality Measurement Background
97(3)
5.2.1 Subjective Methods
97(1)
5.2.2 Intrusive Objective Methods
98(1)
5.2.3 Nonintrusive Objective Methods
99(1)
5.2.4 Bio-Inspired Methods
100(1)
5.3 Modeling Methods
100(6)
5.3.1 Methodology for Conversational Quality Prediction (PESQ/E-model)
100(4)
5.3.2 Nonlinear Surface Regression Model
104(1)
5.3.3 Neural Network Model
105(1)
5.3.4 REPTree Model
105(1)
5.4 Experimental Testbed
106(4)
5.4.1 The Data Sets' Structure
108(2)
5.4.2 The Performance Measures
110(1)
5.5 Results and Discussion
110(3)
5.5.1 Correlation Comparison
110(1)
5.5.2 Residual Analysis
111(2)
5.6 Conclusions
113(4)
References
115(2)
Chapter 6 On the Impact of the Differential Evolution Parameters in the Solution of the Survivable Virtual Topology-Mapping Problem in IP-Over-WDM Networks
117(24)
6.1 Introduction
117(3)
6.2 Problem Formulation
120(1)
6.3 DE Algorithm
121(3)
6.3.1 Fitness of an Individual
123(1)
6.3.2 Pseudocode of the DE Algorithm
123(1)
6.3.3 Enhanced DE-VTM Algorithm
124(1)
6.4 Illustrative Example
124(4)
6.5 Results and Discussion
128(11)
6.6 Conclusions
139(2)
References
139(2)
Chapter 7 Radio Resource Management by Evolutionary Algorithms for 4G LTE-Advanced Networks
141(24)
7.1 Introduction to Radio Resource Management
142(3)
7.1.1 Frame Structure
143(1)
7.1.2 DL and Uplink
143(2)
7.2 LTE-A Technologies
145(2)
7.2.1 Carrier Aggregation
145(1)
7.2.2 Relay Nodes
145(1)
7.2.3 Femtocell
146(1)
7.2.4 Coordinated Multipoint Transmission
146(1)
7.3 Self-Organization Using Evolutionary Algorithms
147(3)
7.3.1 SON Physical Layer
147(1)
7.3.2 SON MAC Layer
148(1)
7.3.3 SON Network Layer
148(1)
7.3.4 LTE-A Open Research Issues and Challenges
149(1)
7.4 EAs in LTE-A
150(11)
7.4.1 Network Planning
151(1)
7.4.2 Network Scheduling
152(1)
7.4.3 Energy Efficiency
153(1)
7.4.4 Load Balancing
154(1)
7.4.5 Resource Allocation
155(6)
7.5 Conclusion
161(4)
References
162(3)
Chapter 8 Robust Transmission for Heterogeneous Networks with Cognitive Small Cells
165(20)
8.1 Introduction
165(2)
8.2 Spectrum Sensing for Cognitive Radio
167(1)
8.3 Underlay Spectrum Sharing
168(2)
8.3.1 Underlay Spectrum Sharing for Heterogeneous Networks with MIMO Channels
169(1)
8.3.2 Underlay Spectrum Sharing for Heterogeneous Networks with Doubly Selective Fading SISO Channels
169(1)
8.4 System Model
170(1)
8.4.1 System Model with MIMO Channel
170(1)
8.4.2 System Model with Doubly Fading Selective SISO Channel
170(1)
8.5 Problem Formulation
171(2)
8.6 Sparsity-Enhanced Mismatch Model (SEMM)
173(2)
8.7 Sparsity-Enhanced Mismatch Model-Reverse DPSS (SEMMR)
175(2)
8.8 Precoder Design Using the SEMM and SEMMR
177(3)
8.8.1 SEMM Precoder Design
177(1)
8.8.2 Second-Stage SEMMR Precoder and Decoder Design
178(2)
8.9 Simulation Results
180(2)
8.9.1 SEMM Precoder
180(1)
8.9.2 SEMMR Transceiver
181(1)
8.10 Conclusion
182(3)
References
183(2)
Chapter 9 Ecologically Inspired Resource Distribution Techniques for Sustainable Communication Networks
185(20)
9.1 Introduction
185(1)
9.2 Consumer-Resource Dynamics
186(2)
9.3 Resource Competition in the NGN
188(4)
9.4 Conditions for Stability and Coexistence
192(3)
9.5 Application for LTE Load Balancing
195(2)
9.6 Validation and Results
197(4)
9.7 Conclusions
201(4)
References
201(4)
Chapter 10 Multiobjective Optimization in Optical Networks
205(40)
10.1 Introduction
206(2)
10.1.1 Common Optical Network Problems in a Multiobjective Context
206(2)
10.2 Multiobjective Optimization
208(7)
10.2.1 Multiobjective Optimization Formulation
208(1)
10.2.2 Multiobjective Performance Metrics
209(1)
10.2.3 Experimental Methodology
210(2)
10.2.4 Algorithms to Solve MOPs
212(3)
10.3 RWA Problem
215(9)
10.3.1 Traditional RWA
215(1)
10.3.2 Multiobjective RWA Formulation
216(1)
10.3.3 ACO for RWA
216(1)
10.3.4 MOACO for RWA
217(3)
10.3.5 Classical Heuristics
220(1)
10.3.6 Simulations
221(1)
10.3.7 Experimental Results
222(2)
10.4 WCA Problem
224(8)
10.4.1 Related Work
225(1)
10.4.2 Classical Problem Formulation
226(1)
10.4.3 Multiobjective Formulation
227(1)
10.4.4 Traffic Models and Simulation Algorithm
227(1)
10.4.5 EA for WCA
228(1)
10.4.6 Experimental Results
229(3)
10.5 p-Cycle Protection
232(7)
10.5.1 Problem Formulation
235(1)
10.5.2 Generating Candidate Cycles
236(1)
10.5.3 Multiobjective Evolutionary Algorithms
237(1)
10.5.4 Experimental Results
238(1)
10.6 Conclusions
239(6)
References
240(5)
Chapter 11 Cell-Coverage-Area Optimization Based on Particle Swarm Optimization (PSO) for Green Macro Long-Term Evolution (LTE) Cellular Networks
245(18)
11.1 Introduction
245(1)
11.2 Related Works
246(2)
11.3 Mechanism of Proposed Cell-Switching Scheme
248(2)
11.4 System Model and Problem Formulation
250(2)
11.5 PSO Algorithm
252(2)
11.6 Simulation Results and Discussion
254(6)
11.6.1 Simulation Setup
254(1)
11.6.2 Simulation Flow Chart
254(1)
11.6.3 Results and Discussion
255(4)
11.6.4 Energy and OPEX Savings
259(1)
11.7 Conclusion
260(3)
Acknowledgment
261(1)
References
261(2)
Chapter 12 Bio-Inspired Computation for Solving the Optimal Coverage Problem in Wireless Sensor Networks: A Binary Particle Swarm Optimization Approach
263(24)
12.1 Introduction
264(2)
12.2 Optimal Coverage Problem in WSN
266(3)
12.2.1 Problem Formulation
266(2)
12.2.2 Related Work
268(1)
12.2.3 Bio-Inspired PSO
269(1)
12.3 BPSO for OCP
269(3)
12.3.1 Solution Representation and Fitness Function
269(1)
12.3.2 Initialization
270(1)
12.3.3 BPSO Operations
271(1)
12.3.4 Maximizing the Disjoint Sets
272(1)
12.4 Experiments and Comparisons
272(10)
12.4.1 Algorithm Configurations
272(1)
12.4.2 Comparisons with State-of-the-Art Approaches
273(1)
12.4.3 Comparisons with the GA Approach
274(2)
12.4.4 Extensive Experiments on Different Scale Networks
276(2)
12.4.5 Results on Maximizing the Disjoint Sets
278(4)
12.5 Conclusion
282(5)
Acknowledgments
282(1)
References
283(4)
Chapter 13 Clonal-Selection-Based Minimum-Interference Channel Assignment Algorithms for Multiradio Wireless mesh Networks
287(36)
13.1 Introduction
288(2)
13.2 Problem Formulation
290(5)
13.2.1 System Model
290(2)
13.2.2 Channel Assignment Problem
292(2)
13.2.3 Related Channel Assignment Algorithms
294(1)
13.3 Clonal-Selection-Based Algorithms for the Channel Assignment Problem
295(9)
13.3.1 Phase One
296(6)
13.3.2 Phase Two
302(1)
13.3.3 Variants of the Channel Assignment Algorithm
303(1)
13.4 Performance Evaluation
304(14)
13.4.1 Comparison with Other Channel Assignment Algorithms
306(2)
13.4.2 Convergence of IA
308(1)
13.4.3 Impact of Parameter Setting
309(3)
13.4.4 Impact of Local Search
312(1)
13.4.5 Variants of Channel Assignment Algorithm
313(5)
13.5 Concluding remarks
318(5)
References
320(3)
Index 323
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022). Chien Su Fong is an associate professor of Engineering & Technology at Multimedia University Malaysia where his research focuses in networking, wireless communications and optical switching technology. He is a frequent presenter at the IEEE International Conference on Communications and a member of the Optical Society of America (OSA). T.O. Ting is currently a Lecturer with the Department of Electrical and Electronic Engineering, Xian Jiaotong-Liverpool University. He obtained his Ph.D in Electrical Engineering from The Hong Kong Polytechnic University. His current research interests focus on the application of Computational Intelligence techniques in Engineering Optimization. He has recently presented his research as an invited speaker at the IEEE Asia Pacific Conference on Circuits and Systems and The Asia-Pacific Conference on Communications.
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